Data processing method and system, and communication device

Abstract

The present disclosure relates to a data processing method and system, and a communication device. The data processing method comprises: a first node receiving first data sent by a second node, wherein the first data is collected by the second node; and the first node sending second data to a third node, wherein the second data is used by the third node to perform model training. In the embodiments of the present disclosure, a second node collects first data and sends same to a first node, and when a third node requires data, the third node can obtain the required data from the first node, such that the data can be reused, and the second node does not repeatedly collect data, thereby avoiding a waste of resources and reducing energy consumption.

Description

Data processing methods, systems and communication equipment Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to data processing methods, systems and communication devices. Background Technology

[0002] Machine learning algorithms are one of the most important methods for implementing artificial intelligence (AI) technology. Models can be trained using large amounts of data, and these models can then be used to predict and reason about events. In many fields, machine learning models can achieve highly accurate predictions. Wireless communication networks can use AI for prediction and reasoning, improving system performance.

[0003] Summary of the Invention

[0004] This disclosure presents data processing methods, systems, and communication devices.

[0005] According to a first aspect of the embodiments of this disclosure, a data processing method is proposed, executed by a first node, the method comprising:

[0006] Receive first data sent by the second node, wherein the first data is collected by the second node;

[0007] Send second data to the third node, wherein the second data is used by the third node for model training.

[0008] According to a second aspect of the embodiments of this disclosure, a data processing method is proposed, executed by a second node, the method comprising:

[0009] The first node sends the collected first data to the first node, wherein the first node is used to send the second data to the third node, and the second data is used by the third node to train the model.

[0010] According to a third aspect of the embodiments of this disclosure, a data processing method is proposed, executed by a third node, the method comprising:

[0011] The third node receives second data sent by the first node, wherein the second data is used for model training.

[0012] According to a fourth aspect of the embodiments of this disclosure, a first node is provided, comprising:

[0013] The transceiver module is configured to receive first data sent by a second node, wherein the first data is collected by the second node; and is further configured to send second data to a third node, wherein the second data is used by the third node for model training.

[0014] According to a fifth aspect of the embodiments of this disclosure, a second node is proposed, comprising:

[0015] The transceiver module is configured to send collected first data to a first node, wherein the first node is used to send second data to a third node, and the second data is used by the third node for model training.

[0016] According to a sixth aspect of the embodiments of this disclosure, a third node is proposed, comprising:

[0017] The transceiver module is configured to receive second data sent by the first node, wherein the second data is used by the third node for model training.

[0018] According to a seventh aspect of the embodiments of this disclosure, a data processing system is provided, comprising:

[0019] The first node is configured to implement the method proposed in the first aspect;

[0020] The second node is configured to implement the method proposed in the second aspect;

[0021] The third node is configured to implement the method proposed in the third aspect.

[0022] According to an eighth aspect of the embodiments of this disclosure, a communication device is provided, comprising:

[0023] One or more processors;

[0024] The communication device is used to execute the method proposed in the first aspect, or the second aspect, or the third aspect.

[0025] According to a ninth aspect of the present disclosure, a storage medium is provided that stores instructions that, when executed on a communication device, cause the communication device to perform the method as proposed in the first, second, or third aspect.

[0026] According to a tenth aspect of the present disclosure, a computer program product is provided, comprising a computer program that, when executed by a communication device, implements the methods as proposed in the first, second, or third aspect.

[0027] In this embodiment of the disclosure, the second node collects and sends the first data to the first node. The first node can store the first data. When the third node needs data, it can obtain the required data from the first node, so that the data can be reused. The second node does not collect data repeatedly, thereby avoiding waste of resources and reducing energy consumption. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0029] Figure 1A is an exemplary schematic diagram of the architecture of a data processing system provided according to an embodiment of the present disclosure.

[0030] Figure 1B is an exemplary schematic diagram of the architecture of a data processing system provided according to an embodiment of the present disclosure.

[0031] Figure 1C is an exemplary schematic diagram of the architecture of a data processing system provided according to an embodiment of the present disclosure.

[0032] Figure 2 is an exemplary interactive schematic diagram of a data processing method provided according to an embodiment of the present disclosure.

[0033] Figure 3A is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0034] Figure 3B is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0035] Figure 3C is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0036] Figure 3D is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0037] Figure 4A is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0038] Figure 4B is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0039] Figure 5A is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0040] Figure 5B is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0041] Figure 6 is an exemplary interactive schematic diagram of a data processing method provided according to an embodiment of the present disclosure.

[0042] Figure 7A is an exemplary schematic diagram of the structure of a first node provided according to an embodiment of the present disclosure.

[0043] Figure 7B is an exemplary schematic diagram of the structure of a first node provided according to an embodiment of the present disclosure.

[0044] Figure 7C is an exemplary schematic diagram of the structure of a first node provided according to an embodiment of the present disclosure.

[0045] Figure 8A is an exemplary schematic diagram of the structure of a communication device provided according to an embodiment of the present disclosure.

[0046] Figure 8B is an exemplary schematic diagram of the structure of a chip provided according to an embodiment of the present disclosure. Detailed Implementation

[0047] This disclosure presents data processing methods, systems, and communication devices.

[0048] In a first aspect, embodiments of this disclosure propose a data processing method, executed by a first node, the method comprising:

[0049] Receive first data sent by the second node, wherein the first data is collected by the second node;

[0050] Send second data to the third node, wherein the second data is used by the third node for model training.

[0051] In the above embodiment, the second node collects and sends the first data to the first node. The first node can store the first data. When the third node needs data, it can obtain the required data from the first node, so that the data can be reused. The second node does not collect data repeatedly, thereby avoiding waste of resources and reducing energy consumption.

[0052] In conjunction with some embodiments of the first aspect, in some embodiments, before receiving the first data sent by the second node, the method further includes:

[0053] Send a first message to the second node, the first message being used to instruct the second node to collect the first data and send the first data to the first node.

[0054] In the above embodiments, the second node can collect and send the first data according to the instructions of the first information. In some implementations, the second node's collection and sending of the first data may be unrelated to the first information.

[0055] In conjunction with some embodiments of the first aspect, in some embodiments, the first information indicates attribute information of a first data attribute, the attribute information of the first data attribute being used by the second node to collect the first data that matches the attribute information of the first data attribute.

[0056] In the above embodiments, the first information indicates the attribute information of the first data attribute, thereby indicating which data the second node collects.

[0057] In conjunction with some embodiments of the first aspect, in some embodiments, the first information indicates a second data attribute, the second data attribute being used by the second node to record attribute information of the second data attribute corresponding to the first data.

[0058] In the above embodiments, the first information indicates which attribute information corresponding to the first data the second node records when collecting the first data.

[0059] In conjunction with some embodiments of the first aspect, in some embodiments, before sending the second data to the third node, the method further includes:

[0060] The first node receives a second message sent by the third node, the second message being used to request the first node to send the second data to the third node.

[0061] In the above embodiments, the third node can send second information to the first node to request data, and the first node can send corresponding second data to the third node according to the request of the second information.

[0062] In conjunction with some embodiments of the first aspect, in some embodiments, the second information indicates attribute information of the third data attribute, and sending the second data to the third node includes:

[0063] Send the second data, which matches the attribute information of the third data attribute, to the third node.

[0064] In the above embodiments, the second information indicates the attribute information of the third data attribute, thereby indicating which data the first node sends.

[0065] In conjunction with some embodiments of the first aspect, in some embodiments, any one of the first data attribute, the second data attribute, and the third data attribute includes at least one of the following:

[0066] Network conditions during data collection;

[0067] The device attributes of the second node;

[0068] Data type;

[0069] Data format.

[0070] In the above embodiments, optional implementation methods for data attributes are defined.

[0071] In conjunction with some embodiments of the first aspect, in some embodiments, the network conditions include at least one of the following:

[0072] Community type;

[0073] Network deployment scenarios;

[0074] Wireless channel quality;

[0075] The frequency of the cell;

[0076] Location of the residential area;

[0077] Distance between base stations;

[0078] Base station antenna configuration;

[0079] Base station transmit power;

[0080] Parameter set.

[0081] In the above embodiments, optional implementation methods for network conditions during data collection are defined.

[0082] In conjunction with some embodiments of the first aspect, in some embodiments, the device attributes include at least one of the following:

[0083] Equipment identification;

[0084] The quality of the collected data;

[0085] Equipment moving speed;

[0086] Equipment location;

[0087] Equipment antenna configuration;

[0088] Equipment rotation speed.

[0089] In the above embodiments, optional implementation methods for the device attributes of the second node are defined.

[0090] In conjunction with some embodiments of the first aspect, in some embodiments, the data type includes one of the following:

[0091] The first type of data is the raw data;

[0092] The second type of data is data that matches the input and output of the model, which is the model that the third node needs to train.

[0093] In the above embodiments, optional implementation methods for data types are defined.

[0094] In conjunction with some embodiments of the first aspect, in some embodiments, the second type of data includes multiple sets of data, each set of data including third data and corresponding fourth data, wherein the third data corresponds to the input data of the model and the fourth data corresponds to the output data of the model.

[0095] In the above embodiments, optional implementation methods for the second type of data are defined.

[0096] In conjunction with some embodiments of the first aspect, in some embodiments, the data format includes at least one of the following:

[0097] Data annotation content;

[0098] Application scenarios;

[0099] The number of data elements included in the third data in each group of the second type of data;

[0100] The number of data elements included in the fourth data in each group of the second type of data;

[0101] Data dimensions.

[0102] In the above embodiments, optional implementation methods for the data format are defined.

[0103] In conjunction with some embodiments of the first aspect, in some embodiments, the data dimension includes at least one of the following:

[0104] Community measurement results;

[0105] Beam measurement results;

[0106] Equipment location;

[0107] Data collection time;

[0108] Time sequence identifiers for data collection;

[0109] Signal-to-interference plus noise ratio (SINR) of the serving cell;

[0110] Has a link failure occurred?

[0111] Synchronization status;

[0112] Out-of-step state;

[0113] Positioning reference signal measurement results;

[0114] Does the measurement and reporting event meet the requirements?

[0115] Equipment moving speed;

[0116] Channel State Information (CSI).

[0117] In the above embodiments, optional implementation methods for data dimensions are defined.

[0118] Secondly, this disclosure provides a data processing method, executed by a second node, the method comprising:

[0119] The first node sends the collected first data to the first node, wherein the first node is used to send the second data to the third node, and the second data is used by the third node to train the model.

[0120] In conjunction with some embodiments of the second aspect, in some embodiments, before sending the first collected data to the first node, the method further includes:

[0121] The first information sent by the first node is received, which instructs the second node to collect the first data and send the first data to the first node.

[0122] In conjunction with some embodiments of the second aspect, in some embodiments, the first information indicates attribute information of the first data attribute, and the method further includes:

[0123] Collect the first data that matches the attribute information of the first data attribute.

[0124] In conjunction with some embodiments of the second aspect, in some embodiments, the first information indicates a second data attribute, and the second data attribute is used by the second node to record attribute information of the second data attribute corresponding to the first data.

[0125] In conjunction with some embodiments of the second aspect, in some embodiments, any one of the data attributes in the first data attribute and the second data attribute includes at least one of the following:

[0126] Network conditions during data collection;

[0127] The device attributes of the second node;

[0128] Data type;

[0129] Data format.

[0130] In conjunction with some embodiments of the second aspect, in some embodiments, the network conditions include at least one of the following:

[0131] Community type;

[0132] Network deployment scenarios;

[0133] Wireless channel quality;

[0134] The frequency of the cell;

[0135] Location of the residential area;

[0136] Distance between base stations;

[0137] Base station antenna configuration;

[0138] Base station transmit power;

[0139] Parameter set.

[0140] In conjunction with some embodiments of the second aspect, in some embodiments, the device attributes include at least one of the following:

[0141] Equipment identification;

[0142] The quality of the collected data;

[0143] Equipment moving speed;

[0144] Equipment location;

[0145] Equipment antenna configuration;

[0146] Equipment rotation speed.

[0147] In conjunction with some embodiments of the second aspect, in some embodiments, the data type includes one of the following:

[0148] The first type of data is the raw data;

[0149] The second type of data is data that matches the input and output of the model, which is the model that the third node needs to train.

[0150] In conjunction with some embodiments of the second aspect, in some embodiments, the second type of data includes multiple sets of data, each set of data including third data and corresponding fourth data, wherein the third data corresponds to the input data of the model and the fourth data corresponds to the output data of the model.

[0151] In conjunction with some embodiments of the second aspect, in some embodiments, the data format includes at least one of the following:

[0152] Data annotation content;

[0153] Application scenarios;

[0154] The number of data elements included in the third data in each group of the second type of data;

[0155] The number of data elements included in the fourth data in each group of the second type of data;

[0156] Data dimensions.

[0157] In conjunction with some embodiments of the second aspect, in some embodiments, the data dimension includes at least one of the following:

[0158] Community measurement results;

[0159] Beam measurement results;

[0160] Equipment location;

[0161] Data collection time;

[0162] Time sequence identifiers for data collection;

[0163] SINR of the service cell;

[0164] Has a link failure occurred?

[0165] Synchronization status;

[0166] Out-of-step state;

[0167] Positioning reference signal measurement results;

[0168] Does the measurement and reporting event meet the requirements?

[0169] Equipment moving speed;

[0170] CSI.

[0171] Thirdly, this disclosure provides a data processing method, executed by a third node, the method comprising:

[0172] The third node receives second data sent by the first node, wherein the second data is used for model training.

[0173] In conjunction with some embodiments of the third aspect, in some embodiments, before receiving the second data sent by the first node, the method further includes:

[0174] Send a second message to the first node, the second message being used to request the first node to send the second data to the third node.

[0175] In conjunction with some embodiments of the third aspect, in some embodiments, the second information indicates attribute information of the third data attribute, and receiving the second data sent by the first node includes:

[0176] Receive the second data sent by the first node that matches the attribute information of the third data attribute.

[0177] In conjunction with some embodiments of the third aspect, in some embodiments, the third data attribute includes at least one of the following:

[0178] Network conditions during data collection;

[0179] The device attributes of the second node;

[0180] Data type;

[0181] Data format.

[0182] In conjunction with some embodiments of the third aspect, in some embodiments, the network conditions include at least one of the following:

[0183] Community type;

[0184] Network deployment scenarios;

[0185] Wireless channel quality;

[0186] The frequency of the cell;

[0187] Location of the residential area;

[0188] Distance between base stations;

[0189] Base station antenna configuration;

[0190] Base station transmit power;

[0191] Parameter set.

[0192] In conjunction with some embodiments of the third aspect, in some embodiments, the device attributes include at least one of the following:

[0193] Equipment identification;

[0194] The quality of the collected data;

[0195] Equipment moving speed;

[0196] Equipment location;

[0197] Equipment antenna configuration;

[0198] Equipment rotation speed.

[0199] In conjunction with some embodiments of the third aspect, in some embodiments, the data type includes one of the following:

[0200] The first type of data is the raw data;

[0201] The second type of data is data that matches the input and output of the model, which is the model that the third node needs to train.

[0202] In conjunction with some embodiments of the third aspect, in some embodiments, the second type of data includes multiple sets of data, each set of data including third data and corresponding fourth data, wherein the third data corresponds to the input data of the model and the fourth data corresponds to the output data of the model.

[0203] In conjunction with some embodiments of the third aspect, in some embodiments, the data format includes at least one of the following:

[0204] Data annotation content;

[0205] Application scenarios;

[0206] The number of data elements included in the third data in each group of the second type of data;

[0207] The number of data elements included in the fourth data in each group of the second type of data;

[0208] Data dimensions.

[0209] In conjunction with some embodiments of the third aspect, in some embodiments, the data dimension includes at least one of the following:

[0210] Community measurement results;

[0211] Beam measurement results;

[0212] Equipment location;

[0213] Data collection time;

[0214] Time sequence identifiers for data collection;

[0215] SINR of the service cell;

[0216] Has a link failure occurred?

[0217] Synchronization status;

[0218] Out-of-step state;

[0219] Positioning reference signal measurement results;

[0220] Does the measurement and reporting event meet the requirements?

[0221] Equipment moving speed;

[0222] CSI.

[0223] Fourthly, embodiments of this disclosure provide a first node, comprising:

[0224] The transceiver module is configured to receive first data sent by a second node, wherein the first data is collected by the second node; and is further configured to send second data to a third node, wherein the second data is used by the third node for model training.

[0225] Fifthly, embodiments of this disclosure provide a second node, comprising:

[0226] The transceiver module is configured to send collected first data to a first node, wherein the first node is used to send second data to a third node, and the second data is used by the third node for model training.

[0227] Sixthly, embodiments of this disclosure provide a third node, including:

[0228] The transceiver module is configured to receive second data sent by the first node, wherein the second data is used by the third node for model training.

[0229] In a seventh aspect, embodiments of this disclosure provide a data processing system, comprising:

[0230] The first node is configured to implement the method described in the optional implementation of the first aspect;

[0231] The second node is configured to implement the method described in the optional implementation of the second aspect;

[0232] The third node is configured to implement the method described in the optional implementation of the third aspect.

[0233] Eighthly, embodiments of this disclosure provide a communication device, comprising:

[0234] One or more processors;

[0235] The communication device is used to perform the method described in the optional implementation of the first aspect, or the second aspect, or the third aspect.

[0236] In a ninth aspect, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the method as described in the optional implementations of the first, second, or third aspects.

[0237] In a tenth aspect, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a communication device, implements the method described in the optional implementations of the first, second, or third aspects.

[0238] Eleventhly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the methods described in the optional implementations of the first, second, or third aspects above.

[0239] Understandably, the aforementioned first node, second node, third node, data processing system, communication equipment, computer program product, chip, or chip system are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0240] This disclosure provides data processing methods, systems, and communication devices. In some embodiments, the terms "data processing method" and "method for providing data services," "communication method," etc., can be used interchangeably; the terms "first node" and "data management node / device," "data storage node / device," etc., can be used interchangeably; the terms "second node" and "data collection node / device," etc., can be used interchangeably; the terms "third node" and "data usage node / device," "model training node / device," etc., can be used interchangeably; and the terms "data processing system" and "system for providing data services," "communication system," etc., can be used interchangeably.

[0241] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0242] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0243] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0244] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0245] In the embodiments disclosed herein, "multiple" refers to two or more.

[0246] In some embodiments, the terms “at least one (at least one item, at least one)”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0247] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0248] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0249] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0250] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0251] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0252] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0253] In some embodiments, the apparatus and device may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "body", etc.

[0254] In some embodiments, "network" can be interpreted as devices included in the network, such as access network devices, core network devices, etc.

[0255] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)," "base station (BS)," "radio base station," or "fixed station." In some embodiments, it may also be understood as "node," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "serving cell," "carrier," "component carrier," or "bandwidth part (BWP)."

[0256] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," etc.

[0257] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0258] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0259] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0260] Machine learning algorithms are one of the most important methods for implementing Artificial Intelligence (AI) technology. Models can be trained using large amounts of data, and these models can then be used to predict and reason about events and data. In many fields, machine learning models can achieve highly accurate predictions. Wireless communication networks can use AI for prediction and reasoning to improve system performance. Training AI models requires collecting vast amounts of data, and the specific data requirements vary depending on the application scenario. For example, application scenarios may include, but are not limited to, beam management, channel state information (CSI) reporting, CSI compression, positioning, handover, mobility management, and radio resource management (RRM) processes in mobile communication systems.

[0261] Data is crucial for AI, and it can be categorized into training data, inference data, and performance monitoring data. Training data is used for model training and testing, inference data is used for model usage, and performance monitoring data is used to monitor model performance, thereby enabling model control or management, such as activation, deactivation, and model switching.

[0262] In some embodiments, the architecture of a data processing system is shown in Figure 1A. In the system shown in Figure 1A, each time a model training node trains a model, it needs to initiate a data collection process, that is, trigger the data collection node to collect the data required for model training. The data collection node sends the collected data to the model training node. When different model training nodes require the same data, such as when different model training nodes train the same model, each model training node will initiate the data collection process, leading to duplicate data collection, which in turn wastes resources and increases energy consumption.

[0263] In some embodiments, the architecture of a data processing system is shown in Figure 1B. In the system shown in Figure 1B, the data management node is responsible for data storage. The data management node receives data from the data collection node and sends data to the model training node. The data collected by the data collection node can be stored in the data management node. When different model training nodes need the same data, such as when different model training nodes are training the same model, each model training node can obtain the required data from the data management node. The data collection node no longer collects data repeatedly, allowing data to be reused, avoiding resource waste, and reducing energy consumption.

[0264] In some embodiments, any of the data management node, data collection node, and model training node can be a terminal or network device (such as a base station, central unit (CU), distributed unit (DU), or other network node).

[0265] In some embodiments, the name of the data management node is not limited to this; for example, it may be called a data management device, data storage node, data storage device, or a first node. In some embodiments, the name of the data collection node is not limited to this; for example, it may be called a data collection device, a data collection device, or a second node. In some embodiments, the name of the model training node is not limited to this; for example, it may be called a data usage node, a data usage device, a model training device, or a third node.

[0266] In some embodiments, the architecture of a data processing system is shown in Figure 1C. As shown in Figure 1C, the data processing system includes a first node 101, a second node 102, and a third node 103. Optionally, the first node 101 can store data. Optionally, the first node 101 receives data from the second node 102. Optionally, the first node 101 sends data to the third node 103. Optionally, there can be one or more first nodes 101. Optionally, there can be one or more second nodes 102. Optionally, there can be one or more third nodes 103.

[0267] In some embodiments, the data (such as first data, second data) mentioned below are training data used for model training or testing and verification.

[0268] In some embodiments, any one of the first node, the second node, and the third node can be a terminal or a network device.

[0269] In some embodiments, the terminal includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0270] In some embodiments, the network device may include an access network device. The access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation evolved Node B (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system.

[0271] In some embodiments, any one of the first node, the second node, and the third node may be a base station.

[0272] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0273] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0274] In some embodiments, any one of the first node, the second node, and the third node can be a CU or a DU.

[0275] In some embodiments, any one of the first node, the second node, and the third node can be another network node.

[0276] In some embodiments, the first node is a base station and the second node is a terminal.

[0277] It is understood that the system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0278] The following embodiments of this disclosure can be applied to the data processing system (or part of the main body) shown in FIG1B or FIG1C, but are not limited thereto. The main bodies shown in FIG1B or FIG1C are illustrative. The data processing system may include all or part of the main bodies in FIG1B or FIG1C, or may include other main bodies other than those in FIG1B or FIG1C. The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0279] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0280] Figure 2 is an interactive schematic diagram of a data processing method according to an embodiment of the present disclosure. As shown in Figure 2, the embodiments of the present disclosure relate to a data processing method, which includes:

[0281] Step S2101: The first node sends the first information to the second node.

[0282] In some embodiments, the first information is used to instruct the second node to collect first data and send the first data to the first node. Optionally, the second node receives the first information. Optionally, the second node collects the first data based on the first information and sends the collected first data to the first node.

[0283] In some embodiments, the first information indicates attribute information of a first data attribute. The attributes that the first data attribute may include are described below. The attribute information of the first data attribute may be denoted as first attribute information or first attribute value, including the attribute values ​​of each attribute in the first data attribute. The second node collects data matching the attribute information of the first data attribute indicated by the first information; this data is the first data. For example, if the first data attribute is the frequency of the cell and the attribute information of the first data attribute is 100MHz, then the second node collects data at the frequency of 100MHz.

[0284] Optionally, the specific content of the data collected by the second node can be related to the application scenario. For example, in beam management, the data collected by the second node may include, but is not limited to, beam measurement results, beam identifiers, the measurement results and identifiers of the K strongest beams, and the acquisition time of the beam measurement results. The measured beams can be configured by the network. For example, in CSI compression, the data collected by the second node may include, but is not limited to, CSI measurement results and the acquisition time of the CSI measurement results. For example, in positioning, the data collected by the second node may include, but is not limited to, channel impulse response measurement results, UE location, and positioning reference signal (PRS) measurement results. For example, in mobility management, the data collected by the second node may include, but is not limited to, the measurement results of the serving cell, the measurement results of the target cell, time, UE location, source cell, and target cell.

[0285] Optionally, the first data collected by the second node includes multiple data elements. For example, in positioning, each data element may include channel impulse response measurement results, UE location, and positioning reference signal measurement results.

[0286] In some embodiments, the first information indicates a second data attribute. The second data attribute is used by the second node to record attribute information of the second data attribute corresponding to the first data. The attributes that the second data attribute may include are described below. The attribute information of the second data attribute can be recorded as second attribute information or second attribute value, including the attribute values ​​of each attribute in the second data attribute. Optionally, when collecting the first data, the second node records the attribute information of the second data attribute corresponding to the first data. For example, if the second data attribute is the base station transmission power, then the second node records the base station's transmission power at that time when collecting the first data.

[0287] In some embodiments, the first information indicates attribute information of a first data attribute, and indicates a second data attribute. For example, according to the above example, when the second node collects data at a frequency of 100MHz, it records the base station's transmission power at that time.

[0288] Optionally, the attributes included in the second data attribute may be the same as, partially the same as, or different from those included in the first data attribute.

[0289] In some embodiments, step S2101 is an optional step. For example, the second node autonomously implements the function indicated by the first information, or the above function is a default or default setting.

[0290] Step S2102: The second node sends the first data to the first node.

[0291] In some embodiments, the second node collects first data and sends the collected first data to the first node. Optionally, the first node receives the first data sent by the second node. Optionally, the first node stores the first data. Because the first node stores the data collected by the second node, it can obtain the required data from the first node when the third node needs data.

[0292] In some embodiments, the second node may collect and send first data according to the instructions of the first information. Optionally, the second node collects the first data according to the first information. Optionally, the first information indicates attribute information of the first data attribute, and the second node sending the first data to the first node includes: the second node sending first data to the first node that matches the attribute information of the first data attribute.

[0293] In some embodiments, the second node may record relevant attribute information corresponding to the first data according to the instruction of the first information. Optionally, the first information indicates a second data attribute, and the second node records the attribute information of the second data attribute corresponding to the first data. Optionally, the second node sends the attribute information of the second data attribute corresponding to the first data to the first node. Optionally, the first node receives the attribute information of the second data attribute corresponding to the first data sent by the second node. Optionally, the first node stores the attribute information of the second data attribute corresponding to the first data.

[0294] In some embodiments, the second node collects and sends the first data, which may be unrelated to the first information.

[0295] Step S2103: The third node sends the second information to the first node.

[0296] In some embodiments, the second information is used to request the first node to send second data to the third node. Optionally, the first node receives the second information sent by the third node.

[0297] In some embodiments, the second information indicates the attribute information of the third data attribute. The attribute information of the third data attribute can be denoted as third attribute information or third attribute value, including the attribute values ​​of each attribute in the third data attribute. Optionally, the attributes included in the third data attribute may be the same as, partially the same as, or different from, the attributes included in the first data attribute. Optionally, the attribute information of the third data attribute may be the same as, partially the same as, or different from, the attribute information of the first data attribute. Optionally, the attributes included in the third data attribute may be the same as, partially the same as, or different from, the attributes included in the second data attribute.

[0298] In some embodiments, any one of the first data attribute, the second data attribute, and the third data attribute may include, but is not limited to, at least one of the following:

[0299] Network conditions during data collection;

[0300] Device attributes of the second node;

[0301] Data type;

[0302] Data format.

[0303] Optionally, the network conditions during data collection refer to the network conditions of the second node when collecting data.

[0304] Optionally, the above network conditions may include, but are not limited to, at least one of the following:

[0305] Community type;

[0306] Network deployment scenarios;

[0307] Wireless channel quality;

[0308] The frequency of the cell;

[0309] Location of the residential area;

[0310] Distance between base stations;

[0311] Base station antenna configuration;

[0312] Base station transmit power;

[0313] Numerology.

[0314] Optionally, if the second node is a terminal, then the aforementioned cell is the serving cell of the terminal.

[0315] Optionally, the cell type may include, but is not limited to, macro, micro, and dense urban cells.

[0316] Optionally, network deployment scenarios may include, but are not limited to, indoor and outdoor environments.

[0317] Alternatively, the quality of the wireless channel can be determined, for example, by reference signal received power (RSRP), reference signal received quality (RSRQ), or signal to interference plus noise ratio (SINR).

[0318] Optionally, the base station antenna configuration may include, but is not limited to, the number of ports, the number of multiple input multiple output (MIMO) layers, etc.

[0319] Optionally, the above-mentioned device attributes may include, but are not limited to, at least one of the following:

[0320] Equipment identification;

[0321] The quality of the collected data;

[0322] Equipment moving speed;

[0323] Equipment location;

[0324] Equipment antenna configuration;

[0325] Equipment rotation speed.

[0326] Optionally, the device identifier can be an identifier determined based on the device manufacturer or device model. Optionally, if the second node is a terminal, the device identifier can also be an identifier assigned to the terminal by the network.

[0327] Optionally, the data quality of the collected data can be the accuracy of the second node's detection, i.e., the degree of consistency between the collected data and the true data value. If the collected data is RSRP, then the accuracy is the difference between the measured RSRP and the actual RSRP. If the collected data is location, then the accuracy is the distance between the measured location and the actual location.

[0328] Optionally, the device location can be a geographical location or a location within a community.

[0329] Optionally, the device antenna configuration may include, but is not limited to, the number of ports.

[0330] Optionally, the above data types may include, but are not limited to, one of the following:

[0331] The first type of data is raw data;

[0332] The second type of data is the data that matches the input and output of the model, which is the model that needs to be trained for the third node.

[0333] Optionally, the raw data can be data elements (samples) collected continuously over a period of time.

[0334] Optionally, the second type of data includes multiple sets of data, each set including a third set of data and a corresponding fourth set of data. The third set of data corresponds to the input data of the model, and the fourth set of data corresponds to the output data of the model. As an example, the model to be trained for the third node is used to predict the afternoon data based on the morning data, such as predicting the afternoon RSRP based on the morning RSRP. The second type of data includes multiple sets of data, with each day's data serving as one set. Each set of data includes a third set of data and a corresponding fourth set of data. The third set of data includes the morning data for that day, and the fourth set of data includes the afternoon data for that day. Optionally, the third set of data includes multiple data elements. Optionally, the fourth set of data includes multiple data elements.

[0335] Optionally, the above data format may include, but is not limited to, at least one of the following:

[0336] Data annotation content;

[0337] Application scenarios;

[0338] The number (or length) of data elements included in the third data in each group of the second type of data;

[0339] The number (or length) of data elements included in the fourth data in each group of the second type of data;

[0340] Data dimensions.

[0341] Optionally, the format of the data annotation content is used to determine the output data of the model. For example, different models may use RSRP as input data, but different models may predict different output data based on RSRP. For instance, model A may predict the location based on RSRP, while model B may predict the RSRP of another cell based on RSRP.

[0342] Optionally, when predicting RRM measurement results, even if the input data is the same, the output data may be different depending on the model and application. The output data may be the measurement results of the cell or the measurement results of the beam.

[0343] Optionally, the application scenarios can include beam management, CSI reporting, CSI compression, positioning, handover, mobility management, RRM, etc.

[0344] Optionally, the number of data elements included in the third data can be determined based on the input data format of the model. Optionally, the length of the observation window can be different when performing RRM prediction; the length of the observation window or the number of data elements in the observation window can be used as the data format. Optionally, the length of the observation window or the number of data elements in the observation window corresponds to the number of data elements included in the third data.

[0345] Optionally, the number of data elements included in the fourth data can be determined based on the output data format of the model. Optionally, the prediction window length can be different when performing RRM prediction; the prediction window length or the number of data elements in the prediction window can be used as the data format. Optionally, the prediction window length or the number of data elements in the prediction window corresponds to the number of data elements included in the fourth data.

[0346] Optionally, data dimensions are used to determine which dimensions of data are included in a data element. As an example, a data element may include data with one dimension, such as RSRP. As another example, a model may have multiple inputs, such as RSRP, location, and time. In this case, a data element may include data with three dimensions, such as RSRP, location, and time. Optionally, data dimensions can be used to determine data elements in a first type of data, or to determine data elements in a second type of data.

[0347] Optionally, the above data dimensions may include, but are not limited to, at least one of the following:

[0348] Community measurement results;

[0349] Beam measurement results;

[0350] Equipment location;

[0351] Data collection time;

[0352] Time sequence identifiers for data collection;

[0353] SINR of the service cell;

[0354] Has a link failure occurred?

[0355] Synchronization status;

[0356] Out-of-step state;

[0357] Positioning reference signal measurement results;

[0358] Does the measurement and reporting event meet the requirements?

[0359] Equipment moving speed;

[0360] CSI.

[0361] In the above embodiments, optional implementation methods for data attributes are defined.

[0362] In some embodiments, step S2103 is an optional step. For example, the first node autonomously implements the function indicated by the second information, or the above function is defaulted or set by default.

[0363] Step S2104: The first node sends the second data to the third node.

[0364] In some embodiments, the first node sends second data to the third node based on second information. The second data is data required by the third node. The second data is used for model training or testing and validation. Optionally, the second information indicates attribute information of a third data attribute, and the first node sends second data to the third node that matches the attribute information of the third data attribute. Optionally, the second data includes multiple data elements.

[0365] In some embodiments, the second data is the first data. Optionally, sending the second data from the first node to the third node includes: the first node sending the first data to the third node.

[0366] In some embodiments, the second data is data obtained based on the first data. For example, the second data is data that has been processed from the first data and meets the requirements of the third node. Data that meets the requirements of the third node is, for example, data that matches the input and output of the model to be trained at the third node. For example, the first data is first-type data, and the second data is second-type data. For example, the second data includes a portion of the data in the first data. For example, the second data may also include data other than the first data.

[0367] In the above embodiment, the second node collects and sends data to the first node. The first node can store the data. When the third node needs data, it can obtain the required data from the first node, so that the data can be reused. The second node does not collect data repeatedly, thereby avoiding waste of resources and reducing energy consumption.

[0368] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0369] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0370] In some embodiments, “get,” “obtain,” “receive,” “transmit,” “bidirectional transmission,” and “send and / or receive” can be used interchangeably and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining through self-processing, or autonomous implementation, among other meanings.

[0371] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0372] The data processing method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2104. For example, step S2101 may be implemented as an independent embodiment, step S2102 may be implemented as an independent embodiment, step S2103 may be implemented as an independent embodiment, step S2104 may be implemented as an independent embodiment, step S2101 + step S2102 may be implemented as an independent embodiment, step S2102 + step S2104 may be implemented as an independent embodiment, step S2103 + step S2104 may be implemented as an independent embodiment, step S2101 + step S2102 + step S2104 may be implemented as an independent embodiment, and step S2102 + step S2103 + step S2104 may be implemented as an independent embodiment, but is not limited thereto.

[0373] In some embodiments, steps S2101 to S2104 may be performed in an alternate order or simultaneously.

[0374] In some embodiments, steps S2101 and S2103 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0375] In some embodiments, other optional implementations described before or after the specification corresponding to FIG2 may be referred to.

[0376] Figure 3A is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 3A, the embodiment of the present disclosure relates to a data processing method, executed by a first node, the method including:

[0377] Step S3101: Send the first message.

[0378] The optional implementation of step S3101 can be found in the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0379] Step S3102: Receive the first data.

[0380] The optional implementation of step S3102 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0381] Step S3103: Receive the second information.

[0382] The optional implementation of step S3103 can be found in the optional implementation of step S2103 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0383] Step S3104: Send the second data.

[0384] The optional implementation of step S3104 can be found in the optional implementation of step S2104 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0385] The data processing method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3104. For example, step S3101 can be implemented as an independent embodiment, step S3102 can be implemented as an independent embodiment, step S3103 can be implemented as an independent embodiment, step S3104 can be implemented as an independent embodiment, step S3101 + step S3102 can be implemented as an independent embodiment, step S3102 + step S3104 can be implemented as an independent embodiment, step S3103 + step S3104 can be implemented as an independent embodiment, step S3101 + step S3102 + step S3104 can be implemented as an independent embodiment, and step S3102 + step S3103 + step S3104 can be implemented as an independent embodiment, but is not limited thereto.

[0386] In some embodiments, steps S3101 to S3104 may be performed in an alternate order or simultaneously.

[0387] In some embodiments, steps S3101 and S3103 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0388] Figure 3B is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 3B, the embodiment of the present disclosure relates to a data processing method, executed by a first node, the method including:

[0389] Step S3201: Send the first message.

[0390] The optional implementation of step S3201 can be found in the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0391] Step S3202: Receive the first data.

[0392] The optional implementation of step S3202 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0393] Step S3203: Send the second data.

[0394] The optional implementation of step S3203 can be found in the optional implementation of step S2104 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0395] The data processing method involved in the embodiments of this disclosure may include at least one of steps S3201 to S3203. For example, step S3201 may be implemented as an independent embodiment, step S3202 may be implemented as an independent embodiment, step S3203 may be implemented as an independent embodiment, step S3201 + step S3202 may be implemented as an independent embodiment, and step S3202 + step S3203 may be implemented as an independent embodiment, but is not limited thereto.

[0396] In some embodiments, steps S3201 to S3203 may be performed in an alternate order or simultaneously.

[0397] In some embodiments, step S3201 is optional and may be omitted or replaced in different embodiments.

[0398] Figure 3C is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 3C, the embodiment of the present disclosure relates to a data processing method, executed by a first node, the method including:

[0399] Step S3301: Receive the first data.

[0400] The optional implementation of step S3301 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0401] Step S3302: Receive the second information.

[0402] The optional implementation of step S3302 can be found in the optional implementation of step S2103 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0403] Step S3303: Send the second data.

[0404] The optional implementation of step S3303 can be found in the optional implementation of step S2104 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0405] The data processing method involved in the embodiments of this disclosure may include at least one of steps S3301 to S3303. For example, step S3301 may be implemented as an independent embodiment, step S3302 may be implemented as an independent embodiment, step S3303 may be implemented as an independent embodiment, step S3302 + step S3303 may be implemented as an independent embodiment, or step S3301 + step S3303 may be implemented as an independent embodiment, but is not limited thereto.

[0406] In some embodiments, steps S3301 to S3303 may be performed in an alternate order or simultaneously.

[0407] In some embodiments, step S3302 is optional and may be omitted or replaced in different embodiments.

[0408] Figure 3D is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 3D, the embodiment of the present disclosure relates to a data processing method, executed by a first node, the method including:

[0409] Step S3401: Receive the first data.

[0410] The optional implementation of step S3401 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0411] Optionally, the first node receives first data sent by the second node. The first data is collected by the second node. Optionally, the first node stores the first data.

[0412] Optionally, the second node collects and sends the first data based on the first information. Optionally, before receiving the first data, the method further includes: sending first information, which instructs the second node to collect the first data and send it to the first node. Optionally, the first information is sent by the first node or by another entity.

[0413] Optionally, the first information indicates the attribute information of the first data attribute, and the attribute information of the first data attribute is used by the second node to collect first data that matches the attribute information of the first data attribute.

[0414] Optionally, the first information indicates a second data attribute, which is used by the second node to record attribute information of the second data attribute corresponding to the first data. Optionally, the method further includes: the first node receiving attribute information of the second data attribute corresponding to the first data. Optionally, the first node storing the attribute information of the second data attribute corresponding to the first data.

[0415] Optionally, the second node collects and sends the first data, which is unrelated to the first information.

[0416] Step S3402: Send the second data.

[0417] The optional implementation of step S3402 can be found in the optional implementation of step S2104 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0418] Optionally, the first node sends second data to the third node. The second data is used by the third node for model training. Optionally, the second data is the first data or data obtained based on the first data.

[0419] Optionally, the first node sends the second data to the third node based on the second information. Optionally, before sending the second data, the method further includes: receiving the second information, which requests the first node to send the second data to the third node. Optionally, the second information is sent by the third node or by another entity.

[0420] Optionally, the second information indicates the attribute information of the third data attribute. The first node sending the second data includes sending second data that matches the attribute information of the third data attribute.

[0421] Optionally, the first node sends second data to the third node, which is unrelated to the second information.

[0422] Optionally, the optional implementation methods of the first data attribute, the second data attribute, and the third data attribute in the above steps can be found in the relevant description in step S2103 of Figure 2, and will not be repeated here.

[0423] Figure 4A is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 4A, the embodiment of the present disclosure relates to a data processing method, executed by a second node, the method including:

[0424] Step S4101: Receive the first information.

[0425] The optional implementation of step S4101 can be found in the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0426] Step S4102: Send the first data.

[0427] The optional implementation of step S4102 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0428] In some embodiments, step S4101 is optional and may be omitted or replaced in different embodiments.

[0429] Figure 4B is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 4B, the embodiment of the present disclosure relates to a data processing method executed by a second node, the method including:

[0430] Step S4201: Send the first data.

[0431] The optional implementation of step S4201 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0432] Optionally, the second node sends the collected first data to the first node. The first node receives the first data sent by the second node. Optionally, the first node stores the first data. The first node is used to send second data to the third node, and the second data is used by the third node for model training. Optionally, the second data is the first data or data obtained based on the first data.

[0433] Optionally, the second node collects and sends the first data based on the first information. Optionally, before sending the first data, the method further includes: receiving first information, the first information being used to instruct the second node to collect the first data and send the first data to the first node. Optionally, the first information is sent by the first node, or by another entity.

[0434] Optionally, the first information indicates attribute information of a first data attribute, and the attribute information of the first data attribute is used by the second node to collect first data that matches the attribute information of the first data attribute. Optionally, the method further includes: collecting first data that matches the attribute information of the first data attribute.

[0435] Optionally, the first information indicates a second data attribute, which is used by the second node to record attribute information of the second data attribute corresponding to the first data. Optionally, the method further includes: recording attribute information of the second data attribute corresponding to the first data when collecting the first data. Optionally, the method further includes: sending attribute information of the second data attribute corresponding to the first data to the first node. Optionally, the first node receives attribute information of the second data attribute corresponding to the first data. Optionally, the first node stores attribute information of the second data attribute corresponding to the first data.

[0436] Optionally, the second node collects and sends the first data, which is unrelated to the first information.

[0437] Optionally, the optional implementation methods of the first data attribute and the second data attribute in the above steps can be found in the relevant description in step S2103 of Figure 2, and will not be repeated here.

[0438] Figure 5A is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 5A, the embodiment of the present disclosure relates to a data processing method executed by a third node, the method including:

[0439] Step S5101: Send the second message.

[0440] The optional implementation of step S5101 can be found in the optional implementation of step S2103 in Figure 2, as well as other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0441] Step S5102: Receive the second data.

[0442] The optional implementation of step S5102 can be found in the optional implementation of step S2104 in Figure 2, as well as other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0443] In some embodiments, step S5101 is optional and may be omitted or replaced in different embodiments.

[0444] Figure 5B is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 5B, the embodiment of the present disclosure relates to a data processing method executed by a third node, the method including:

[0445] Step S5201: Receive the second data.

[0446] The optional implementation of step S5201 can be found in the optional implementation of step S2104 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0447] Optionally, the third node receives second data sent by the first node. The second data is used by the third node for model training. Optionally, the second data is the first data or data obtained based on the first data, wherein the first data is collected by the second node and sent to the first node by the second node.

[0448] Optionally, the first node sends the second data to the third node based on the second information. Optionally, before receiving the second data, the method further includes: sending the second information, which requests the first node to send the second data to the third node. Optionally, the second information is sent by the third node or by another entity.

[0449] Optionally, the second information indicates the attribute information of the third data attribute. The third node receiving the second data includes: receiving second data that matches the attribute information of the third data attribute.

[0450] Optionally, the first node sends second data to the third node, which is unrelated to the second information.

[0451] Optionally, the alternative implementation methods of the third data attribute in the above steps can be found in the relevant description in step S2103 of Figure 2, and will not be repeated here.

[0452] Figure 6 is an interactive schematic diagram of a data processing method according to an embodiment of the present disclosure. As shown in Figure 6, the embodiments of the present disclosure relate to a data processing method, which includes:

[0453] Step S6101: The second node sends the first data to the first node.

[0454] The optional implementation of step S6101 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0455] Step S6102: The first node sends the second data to the third node.

[0456] The optional implementation of step S6102 can be found in the optional implementation of step S2104 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0457] Based on the embodiments of this disclosure and in conjunction with the architecture of the data processing system shown in FIG1B, the data processing method proposed in the embodiments of this disclosure is described below.

[0458] Referring to the system shown in Figure 1B, the data management node is responsible for data storage. The data management node receives data from the data collection node (the device that collects data) and sends data to the model training node.

[0459] As an example, the above data can be training data, which is used for model training or testing and verification.

[0460] As an example, the data management node, data collection node, and model training node can be a UE, base station, CU, DU, or other network node. For example, the data management node can be a base station, and the data collection node can be a UE.

[0461] As an example, the data management node sends a first message to the data collection node, the first message instructing the data collection node to collect and report data.

[0462] As an example, the first information may also indicate the attribute information of the data, and the data collection node collects data that matches the attribute information when collecting data.

[0463] As an example, the first information can also indicate the attributes of the data, and the data collection node collects the attribute information corresponding to the data records.

[0464] As an example, the data collection node sends the collected data to the data management node.

[0465] As an example, the data management node receives second information sent by the model training node, the second information indicating a request for data.

[0466] As an example, the second information can also indicate the attribute information of the data, and the data management node sends the data that matches the attribute information to the model training node.

[0467] Optionally, the attributes of the data include any of the following:

[0468] Network conditions during data collection;

[0469] Device attributes for collecting data;

[0470] Data type;

[0471] Data format.

[0472] Alternatively, network conditions may include any of the following:

[0473] Community types, such as macro, micro, and dense urban communities;

[0474] Network deployment scenarios, such as indoors and outdoors;

[0475] Wireless channel quality can be determined by RSRP, RSRQ, or SINR.

[0476] The frequency of the cell;

[0477] Location of the residential area;

[0478] Distance between base stations;

[0479] Antenna configuration, including the number of ports and the number of MIMO layers;

[0480] Transmission power;

[0481] Numerology.

[0482] As an example, the antenna configuration in the above network conditions is the antenna configuration on the network side.

[0483] Optionally, the device attributes for collecting data may include any of the following:

[0484] Identification of the device collecting the data;

[0485] The quality of the collected data;

[0486] The speed at which the equipment moves;

[0487] The location of the device can be a geographical location or a location within a residential community;

[0488] Antenna configuration, including the number of ports;

[0489] Rotational speed.

[0490] As an example, the device identifier can be determined based on the device manufacturer or device model. If the device collecting data is a UE, the device identifier can also be assigned by the network to the UE.

[0491] As an example, data quality can be the accuracy of the detection, i.e., the degree of consistency between the collected data and the true data value. If the collected data is RSRP, then the accuracy can be the difference between the measured RSRP and the actual RSRP. If the collected data is location, then the accuracy can be the distance between the measured geographic coordinates and the actual location coordinates.

[0492] As an example, the antenna configuration in the above device attributes refers to the antenna configuration of the device collecting data. If the device collecting data is a UE, then the antenna configuration in the above device attributes refers to the antenna configuration of that UE. If the device collecting data is a base station, then the above network conditions and the antenna configuration in the device attributes may be the same.

[0493] Alternatively, the data type may include any of the following:

[0494] The first type of data is raw data;

[0495] The second type of data is data that matches the model's input and output.

[0496] As an example, the raw data can be data elements (samples) collected continuously over a period of time.

[0497] As an example, the data matching the model input and output consists of multiple sets of data. Each set of data is divided into a third set of data and a corresponding fourth set of data, which correspond to the model's input and output data, respectively.

[0498] Alternatively, the data format may include any of the following:

[0499] Data annotation content;

[0500] Application scenarios;

[0501] The number / length of data elements included in the third data in each group of the second type of data;

[0502] The number / length of data elements included in the fourth data in each group of the second type of data;

[0503] The dimensions of data are used to determine what data is included in each data element.

[0504] As an example, the format of the data annotation content is used to determine the output data.

[0505] As an example, when predicting RRM measurement results, even with the same input, the output data can differ depending on the model and application. The output data can be the measurement results of the cell or the measurement results of the beam.

[0506] As an example, application scenarios can include beam management, CSI reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0507] As an example, the data format may include the length of the data, which can be determined based on the input data format of the model.

[0508] As an example, the length of the observation window can vary when performing RRM prediction. The length of the observation window or the number of data points in the observation window can be used as the data format. Optionally, the length of the observation window or the number of data points in the observation window corresponds to the number / length of data elements included in the third data.

[0509] As an example, the data format may include the length of the data, which can be determined based on the output data format of the model.

[0510] As an example, when performing RRM prediction, the length of the prediction window can be different, and the length of the prediction window or the number of data in the prediction window can be used as the data format. Optionally, the length of the prediction window or the number of data in the prediction window corresponds to the number / length of data elements included in the fourth data.

[0511] As an example, the dimensions of the data can be used to determine data elements in the first type of data, or to determine data elements in the third type of data in the second type of data.

[0512] As an example, the dimensions of the data may include any of the following: cell measurement results, beam measurement results, data collection device location, data collection time, data collection timing identifier, serving cell SINR, whether a link failure has occurred, synchronization status, out-of-synchronization status, positioning reference signal measurement results, whether measurement reporting events are satisfied, device movement speed, CSI information, etc.

[0513] In the embodiments disclosed herein, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations in other embodiments.

[0514] This disclosure also provides an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0515] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0516] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be hardware circuits designed for artificial intelligence, which can be understood as ASICs, such as Neural Network Processing Units (NPUs), Tensor Processing Units (TPUs), and Deep Learning Processing Units (DPUs).

[0517] Figure 7A is a schematic diagram of the structure of the first node proposed in an embodiment of this disclosure. As shown in Figure 7A, the first node 7100 may include at least one of a transceiver module 7101, a processing module 7102, etc. In some embodiments, the transceiver module 7101 is used to receive first data sent by a second node, wherein the first data is collected by the second node; and is also used to send second data to a third node, wherein the second data is used by the third node for model training. Optionally, the transceiver module is used to perform at least one of the communication steps (e.g., steps S2101, S2104, but not limited thereto) performed by the first node in any of the above methods, which will not be described in detail here. Optionally, the processing module is used to perform at least one of the other steps performed by the first node in any of the above methods, which will not be described in detail here.

[0518] Figure 7B is a schematic diagram of the structure of the second node proposed in an embodiment of this disclosure. As shown in Figure 7B, the second node 7200 may include at least one of a transceiver module 7201, a processing module 7202, etc. In some embodiments, the transceiver module 7201 is used to send collected first data to a first node, wherein the first node is used to send second data to a third node, and the second data is used by the third node for model training. Optionally, the transceiver module is used to perform at least one of the communication steps (e.g., step S2102, but not limited thereto) performed by the second node in any of the above methods, which will not be described in detail here. Optionally, the processing module is used to perform at least one of the other steps performed by the second node in any of the above methods, which will not be described in detail here.

[0519] Figure 7C is a schematic diagram of the structure of the third node proposed in an embodiment of this disclosure. As shown in Figure 7C, the third node 7300 may include at least one of a transceiver module 7301, a processing module 7302, etc. In some embodiments, the transceiver module 7301 is used to receive second data sent by the first node, wherein the second data is used by the third node for model training. Optionally, the transceiver module is used to perform at least one of the communication steps (e.g., step S2103, but not limited thereto) performed by the third node in any of the above methods, which will not be described in detail here. Optionally, the processing module is used to perform at least one of the other steps performed by the third node in any of the above methods, which will not be described in detail here.

[0520] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0521] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module. Optionally, the processing module may be interchangeable with a processor.

[0522] Figure 8A is a schematic diagram of the structure of the communication device 8100 proposed in an embodiment of this disclosure. The communication device 8100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 8100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0523] As shown in Figure 8A, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. The communication device 8100 is used to execute any of the above methods.

[0524] In some embodiments, the communication device 8100 further includes one or more memories 8102 for storing instructions. Optionally, all or part of the memories 8102 may also be located outside the communication device 8100.

[0525] In some embodiments, the communication device 8100 further includes one or more transceivers 8103. When the communication device 8100 includes one or more transceivers 8103, the transceivers 8103 perform at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps S2101 to S2104, but not limited thereto), and the processor 8101 performs at least one of the other steps.

[0526] In some embodiments, a transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc., may be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., may be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., may be used interchangeably.

[0527] In some embodiments, the communication device 8100 may include one or more interface circuits 8104. Optionally, the interface circuit 8104 is connected to the memory 8102, and the interface circuit 8104 can be used to receive signals from the memory 8102 or other devices, and can be used to send signals to the memory 8102 or other devices. For example, the interface circuit 8104 can read instructions stored in the memory 8102 and send the instructions to the processor 8101.

[0528] The communication device 8100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 8100 described in this disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG8A. The communication device may be a standalone device or may be part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0529] Figure 8B is a schematic diagram of the structure of chip 8200 according to an embodiment of this disclosure. For cases where the communication device 8100 can be a chip or a chip system, please refer to the schematic diagram of chip 8200 shown in Figure 8B, but it is not limited thereto.

[0530] Chip 8200 includes one or more processors 8201, which are used to perform any of the above methods.

[0531] In some embodiments, chip 8200 further includes one or more interface circuits 8202. Optionally, the interface circuit 8202 is connected to memory 8203, and the interface circuit 8202 can be used to receive signals from memory 8203 or other devices, and the interface circuit 8202 can be used to send signals to memory 8203 or other devices. For example, the interface circuit 8202 can read instructions stored in memory 8203 and send the instructions to processor 8201.

[0532] In some embodiments, the interface circuit 8202 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps S2101 to S2104, but not limited thereto), and the processor 8201 performs at least one of the other steps.

[0533] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0534] In some embodiments, chip 8200 further includes one or more memories 8203 for storing instructions. Optionally, all or part of the memories 8203 may be located outside of chip 8200.

[0535] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device 8100, cause the communication device 8100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0536] This disclosure also provides a program product that, when executed by the communication device 8100, causes the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0537] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A data processing method, characterized in that, Executed by the first node, the method includes: Receive first data sent by the second node, wherein the first data is collected by the second node; Send second data to the third node, wherein the second data is used by the third node for model training.

2. The method according to claim 1, characterized in that, Before receiving the first data sent by the second node, the method further includes: Send a first message to the second node, the first message being used to instruct the second node to collect the first data and send the first data to the first node.

3. The method according to claim 2, characterized in that, The first information indicates the attribute information of the first data attribute, and the attribute information of the first data attribute is used by the second node to collect the first data that matches the attribute information of the first data attribute.

4. The method according to claim 2 or 3, characterized in that, The first information indicates the second data attribute, which is used by the second node to record the attribute information of the second data attribute corresponding to the first data.

5. The method according to any one of claims 1-4, characterized in that, Before sending the second data to the third node, the method further includes: The first node receives a second message sent by the third node, the second message being used to request the first node to send the second data to the third node.

6. The method according to claim 5, characterized in that, The second information indicates the attribute information of the third data attribute, and the step of sending the second data to the third node includes: Send the second data, which matches the attribute information of the third data attribute, to the third node.

7. The method according to any one of claims 3, 4, and 6, characterized in that, Any one of the first data attribute, the second data attribute, and the third data attribute includes at least one of the following: Network conditions during data collection; The device attributes of the second node; Data type; Data format.

8. The method according to claim 7, characterized in that, The network conditions include at least one of the following: Community type; Network deployment scenarios; Wireless channel quality; The frequency of the cell; Location of the residential area; Distance between base stations; Base station antenna configuration; Base station transmit power; Parameter set.

9. The method according to claim 7 or 8, characterized in that, The device attributes include at least one of the following: Equipment identification; The quality of the collected data; Equipment moving speed; Equipment location; Equipment antenna configuration; Equipment rotation speed.

10. The method according to any one of claims 7-9, characterized in that, The data type includes one of the following: The first type of data is the raw data; The second type of data is data that matches the input and output of the model, which is the model that the third node needs to train.

11. The method according to claim 10, characterized in that, The second type of data includes multiple sets of data, each set of data including a third set of data and a corresponding fourth set of data. The third set of data corresponds to the input data of the model, and the fourth set of data corresponds to the output data of the model.

12. The method according to any one of claims 7-11, characterized in that, The data format includes at least one of the following: Data annotation content; Application scenarios; The number of data elements included in the third data in each group of the second type of data; The number of data elements included in the fourth data in each group of the second type of data; Data dimensions.

13. The method according to claim 12, characterized in that, The data dimension includes at least one of the following: Community measurement results; Beam measurement results; Equipment location; Data collection time; Time sequence identifiers for data collection; Signal-to-interference-plus-noise ratio (SINR) of the serving cell; Has a link failure occurred? Synchronization status; Out-of-step state; Positioning reference signal measurement results; Does the measurement and reporting event meet the requirements? Equipment moving speed; Channel State Information (CSI).

14. A data processing method, characterized in that, Executed by the second node, the method includes: The first node sends the collected first data to the first node, wherein the first node is used to send the second data to the third node, and the second data is used by the third node to train the model.

15. The method according to claim 14, characterized in that, Before sending the first collected data to the first node, the method further includes: The first information sent by the first node is received, which instructs the second node to collect the first data and send the first data to the first node.

16. The method according to claim 15, characterized in that, The first information indicates attribute information of the first data attribute, and the method further includes: Collect the first data that matches the attribute information of the first data attribute.

17. The method according to claim 15 or 16, characterized in that, The first information indicates the second data attribute, which is used by the second node to record the attribute information of the second data attribute corresponding to the first data.

18. The method according to claim 16 or 17, characterized in that, The first data attribute and the second data attribute each include at least one of the following: Network conditions during data collection; The device attributes of the second node; Data type; Data format.

19. The method according to claim 18, characterized in that, The network conditions include at least one of the following: Community type; Network deployment scenarios; Wireless channel quality; The frequency of the cell; Location of the residential area; Distance between base stations; Base station antenna configuration; Base station transmit power; Parameter set.

20. The method according to claim 18 or 19, characterized in that, The device attributes include at least one of the following: Equipment identification; The quality of the collected data; Equipment moving speed; Equipment location; Equipment antenna configuration; Equipment rotation speed.

21. The method according to any one of claims 18-20, characterized in that, The data type includes one of the following: The first type of data is the raw data; The second type of data is data that matches the input and output of the model, which is the model that the third node needs to train.

22. The method according to claim 21, characterized in that, The second type of data includes multiple sets of data, each set of data including a third set of data and a corresponding fourth set of data. The third set of data corresponds to the input data of the model, and the fourth set of data corresponds to the output data of the model.

23. The method according to any one of claims 18-22, characterized in that, The data format includes at least one of the following: Data annotation content; Application scenarios; The number of data elements included in the third data in each group of the second type of data; The number of data elements included in the fourth data in each group of the second type of data; Data dimensions.

24. The method according to claim 23, characterized in that, The data dimension includes at least one of the following: Community measurement results; Beam measurement results; Equipment location; Data collection time; Time sequence identifiers for data collection; SINR of the service cell; Has a link failure occurred? Synchronization status; Out-of-step state; Positioning reference signal measurement results; Does the measurement and reporting event meet the requirements? Equipment moving speed; CSI.

25. A data processing method, characterized in that, Executed by a third node, the method includes: The third node receives second data sent by the first node, wherein the second data is used for model training.

26. The method according to claim 25, characterized in that, Before receiving the second data sent by the first node, the method further includes: Send a second message to the first node, the second message being used to request the first node to send the second data to the third node.

27. The method according to claim 26, characterized in that, The second information indicates the attribute information of the third data attribute, and the receiving of the second data sent by the first node includes: Receive the second data sent by the first node that matches the attribute information of the third data attribute.

28. The method according to claim 27, characterized in that, The third data attribute includes at least one of the following: Network conditions during data collection; The device attributes of the second node; Data type; Data format.

29. The method according to claim 28, characterized in that, The network conditions include at least one of the following: Community type; Network deployment scenarios; Wireless channel quality; The frequency of the cell; Location of the residential area; Distance between base stations; Base station antenna configuration; Base station transmit power; Parameter set.

30. The method according to claim 28 or 29, characterized in that, The device attributes include at least one of the following: Equipment identification; The quality of the collected data; Equipment moving speed; Equipment location; Equipment antenna configuration; Equipment rotation speed.

31. The method according to any one of claims 28-30, characterized in that, The data type includes one of the following: The first type of data is the raw data; The second type of data is data that matches the input and output of the model, which is the model that the third node needs to train.

32. The method according to claim 31, characterized in that, The second type of data includes multiple sets of data, each set of data including a third set of data and a corresponding fourth set of data. The third set of data corresponds to the input data of the model, and the fourth set of data corresponds to the output data of the model.

33. The method according to any one of claims 28-32, characterized in that, The data format includes at least one of the following: Data annotation content; Application scenarios; The number of data elements included in the third data in each group of the second type of data; The number of data elements included in the fourth data in each group of the second type of data; Data dimensions.

34. The method according to claim 33, characterized in that, The data dimension includes at least one of the following: Community measurement results; Beam measurement results; Equipment location; Data collection time; Time sequence identifiers for data collection; SINR of the service cell; Has a link failure occurred? Synchronization status; Out-of-step state; Positioning reference signal measurement results; Does the measurement and reporting event meet the requirements? Equipment moving speed; CSI.

35. A first node, characterized in that, include: The transceiver module is configured to receive first data sent by a second node, wherein the first data is collected by the second node; and is further configured to send second data to a third node, wherein the second data is used by the third node for model training.

36. A second node, characterized in that, include: The transceiver module is configured to send collected first data to a first node, wherein the first node is used to send second data to a third node, and the second data is used by the third node for model training.

37. A third node, characterized in that, include: The transceiver module is configured to receive second data sent by the first node, wherein the second data is used by the third node for model training.

38. A data processing system, characterized in that, include: The first node is configured to implement the method of any one of claims 1-13; The second node is configured to implement the method of any one of claims 14-24; The third node is configured to implement the method of any one of claims 25-34.

39. A communication device, characterized in that, include: One or more processors; The communication device is used to perform the method according to any one of claims 1-34.

40. A storage medium storing instructions, characterized in that, When the instructions are executed on the communication device, the communication device performs the method as described in any one of claims 1-34.

41. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the communication device, it implements the method as described in any one of claims 1-34.

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